Instructions to use EdBergJr/layoutlm-funsd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EdBergJr/layoutlm-funsd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="EdBergJr/layoutlm-funsd")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("EdBergJr/layoutlm-funsd") model = AutoModelForTokenClassification.from_pretrained("EdBergJr/layoutlm-funsd", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 90c9c916d00bc32ea6fcfe563533d3a1552f5c1dd49d42d98c4407cb82024381
- Size of remote file:
- 451 MB
- SHA256:
- 3bc174e190a860488f9d015c25f0e6bd78f0e6abb89e48147b59e26dd2fe7155
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